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A chromosomal duplication that includes the canine microsatellite <i>INRA21</i> in Labrador Retrievers

2008· article· en· W1951989883 on OpenAlexaboutno aff
Akio Onogi, Masanobu Nurimoto, Yuko Sato, Mitsuo MORITA

Bibliographic record

VenueAnimal Genetics · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomic variations and chromosomal abnormalities
Canadian institutionsnot available
Fundersnot available
KeywordsGene duplicationBiologyMicrosatelliteGenotypingGeneticsLabrador RetrieverSegmental duplicationGenotypeGenePathologyAlleleGenomeMedicine

Abstract

fetched live from OpenAlex

INRA21 is one of the canine microsatellites recommended for parentage verification by the International Society for Animal Genetics. In Labrador Retrievers, abnormal peak patterns such as three-peak patterns during capillary electrophoresis were frequently observed at INRA21. Pedigree analysis indicated that the abnormal peak patterns were due to inheritable causes, and semiquantitative multiplex (SQM) PCR analysis showed that the abnormal peak patterns were caused by chromosomal duplication. Walking SQM-PCR analysis revealed that the size of the duplicated segment was approximately 1.58 Mb. Genotypes of microsatellites within the duplicated segment indicated that the duplication was an identical-by-descent mutation. This duplication is probably carried by more than half of the dogs in the Japanese population of Labrador Retrievers. The abnormal peak patterns at INRA21 were also observed in German Shorthaired Pointers and Flat-Coated Retrievers. Genotyping analysis of the microsatellites within the duplicated segment in Labrador Retrievers suggested that the abnormal peak patterns observed in the two breeds were due to the duplication inherited from the same ancestor as the duplication of Labrador Retrievers. This study urges attention to the use of INRA21 and shows an example of copy number polymorphisms that are characteristic to dog breeds or lineages.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.223
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2008
Admission routes1
Has abstractyes

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